The tedious task of finding homologous noncoding RNA genes

Menzel, Peter; Gorodkin, Jan; Stadler, Peter F.

doi:10.1261/rna.1556009

Cited by 50 publications

(42 citation statements)

References 63 publications

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“…Thus, it will be a powerful tool for the annotation of future non-coding transcriptomes in this era of genomic progress, which complements other currently available comparative genomics methodologies. Our approach may even outperform homology-based methods, given the lower homology owing to compensatory evolution in many classes of RNAs (29). …”

Section: Discussionmentioning

confidence: 99%

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

Leung

Ryvkin²,

Ungar³

et al. 2013

Nucleic Acids Research

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The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.

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Section: Discussionmentioning

confidence: 99%

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

Leung

Ryvkin²,

Ungar³

et al. 2013

Nucleic Acids Research

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“…Curating these and conducting homology-based searches comes with its own set of issues, which is described elsewhere [7].…”

Section: Sequence Similarity Search For Ncrnasmentioning

confidence: 99%

“…An abundant class of ncRNA genes are the micro RNAs (miRNAs), which have received considerable attention e.g., [3]–[5]. This can be observed through the rapid growth in the literature, not only for miRNAs [6], but also for ncRNAs in general [7]. Furthermore, regulatory RNA structure in UTR regions of protein-coding genes is also an exciting, emerging field.…”

Section: Introductionmentioning

confidence: 99%

From Structure Prediction to Genomic Screens for Novel Non-Coding RNAs

Gorodkin

Hofacker

2011

PLoS Comput Biol

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Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

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“…In spite of considerable improvements in the development of ncRNA detection software in the recent years, the prediction and annotation of ncRNAs still remain challenging tasks (Menzel et al 2009). The most effective ncRNA detection software are distributed as stand-alone applications Klein and Eddy 2003;Livny et al 2005;Washietl et al 2005), some of them address only specific classes of RNAs (Lowe and Eddy 1997;Laslett and Canbäck 2004;Schattner et al 2006;Lagesen et al 2007) or organisms (Klein et al 2002;Schattner 2002;Upadhyay et al 2005;Larsson et al 2008).…”

Section: Introductionmentioning

confidence: 99%

RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA

Cros¹,

Monte²,

Mariette³

et al. 2011

RNA

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The annotation of noncoding RNA genes remains a major bottleneck in genome sequencing projects. Most genome sequences released today still come with sets of tRNAs and rRNAs as the only annotated RNA elements, ignoring hundreds of other RNA families. We have developed a web environment that is dedicated to noncoding RNA (ncRNA) prediction, annotation, and analysis and allows users to run a variety of tools in an integrated and flexible manner. This environment offers complementary ncRNA gene finders and a set of tools for the comparison, visualization, editing, and export of ncRNA candidates. Predictions can be filtered according to a large set of characteristics. Based on this environment, we created a public website located at http://RNAspace.org. It accepts genomic sequences up to 5 Mb, which permits for an online annotation of a complete bacterial genome or a small eukaryotic chromosome. The project is hosted as a Source Forge project (http://rnaspace.sourceforge.net/).

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The tedious task of finding homologous noncoding RNA genes

Cited by 50 publications

References 63 publications

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

From Structure Prediction to Genomic Screens for Novel Non-Coding RNAs

RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA

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